The subject disclosure is directed to transportation arts, the data processing arts, the data analysis arts, the tracking arts, the predictive arts, and the like.
Intelligent transportation systems generally include multiple vehicles, routes, and services that are utilized by a large number of users. Efficient planning and management of transportation networks, in particular, e.g., an adequate response to changing traffic conditions, require an accurate modeling and real-time prediction of these time-dependent entities. The ability to track and analyze various time-dependent events, such as vehicle position, road load, travel time and demand, traffic density, etc., represent some of the factors that must be taken into consideration during transportation planning and management. Accordingly, efficient planning of transport services requires an accurate estimation of the number of travelers entering the network at any period of time, commonly referenced as the travel demand on a public transportation network.
Currently, prediction of travel demand requires the collection of information relative to a variety of segments of the transportation network, each segment referring to a particular route, a vehicle, a stop, a series of stops, etc. Each must individually be modeled to properly predict a demand on that segment, and aggregation is then performed for the overall demand on a system. That is, each individual segment requires substantial computation and analysis for effective and efficient management. Additionally, the amount of data available for disparate segments may be different.
Any entity that changes over time may be represented as a time series, and travel demand on a transportation system does vary over time. One problem often encountered in planning and management of transportation networks is the modeling and prediction of a series of temporal events. For example, the number of passengers on a particular vehicle may vary over the course of a day, the number of travelers on the network may vary over the course of a week, the number of travelers at a particular stop may vary over the course of hours, and the like. This problem is generally compounded when variable amounts of data are received for different portions of the network, i.e., one vehicle may have better recording sensors or devices than another, a vehicle may lack recording devices, but stops do, and the like.
Current transportation network planning and management may fail to account for various factors that affect this series of temporal events. For example, weather impacts the number of users of a public transportation system. When it is raining, the number of travelers on the public transportation system may drop, as those with cars will take their personal vehicles to avoid walking or waiting in the rain. Similarly, the time or day or day of the week may also impact demand on segments of the transportation network. For example, the time of day may impact the demand (i.e., rush hour), and the day of the week (i.e., lower travel on weekends) impact any prediction or modeling. Traffic on the transportation network may also factor in modeling and prediction, as heavy automobile traffic may increase demand on the public transportation network, construction, etc. Such series of temporal events may be related to each other, but current methodologies do not factor this when modeling and predicting demand.
For example, FIGS. 1A-1B depicts three time series representing passenger load at different routes in a particular city. In each series, the number of passengers boarding public vehicles during a certain period of time is shown over the course of a day and then over the course of a week. As depicted in FIG. 1A, each route varies over the course of a day, and as depicted in FIG. 1B, each route varies over the course of a week. In conventional planning and management, each of these entities are modeled differently, i.e., different tasks must be performed.
Multi-task learning is a form of inductive transfer, machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different, but related problem. That is, multi-task learning is aimed at leveraging the information of multiple, mutually related learning tasks to make more accurate predictions for the individual tasks. Related information contained in task can be exploited to mutually increase the quality of predictions. For example, multi-task learning has been applied to several different domains, e.g., computational biology, natural language processing, computer vision, and the like, where multiple biological, textual and visual object classes may share some of the relevant features. In multi-task learning, the prediction accuracy in each task is leveraged by making use of data from the other tasks, e.g., regularization, mutualization, and the like.
Thus, it would be advantageous to provide an efficient system and method for predicting travel demand across a transportation network utilizing multi-task learning.